{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tongyi.embeddings import TongyiEmbeddings\n",
    "\n",
    "embeddings_model = TongyiEmbeddings()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 1536)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "texts = [\n",
    "        \"你好吗\",\n",
    "        \"你的名字是什么\",\n",
    "        \"我的肚子好痛啊\",\n",
    "        \"肠胃不舒服\",\n",
    "        \"我在吃东西\"\n",
    "    ]\n",
    "embeddings = embeddings_model.embed_documents(texts)\n",
    "\n",
    "len(embeddings), len(embeddings[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.3016113340854645,\n",
       " 1.5886719226837158,\n",
       " 0.846020519733429,\n",
       " -0.800488293170929,\n",
       " -1.6886475086212158,\n",
       " 0.27827149629592896,\n",
       " 1.8710448741912842,\n",
       " -1.1935546398162842,\n",
       " -1.3988037109375,\n",
       " 0.968017578125]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings[0][:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# cosine similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"我的肚子好痛啊\"与\"你好吗\"的语义相似度为：0.3540708666322656\n",
      "\"我的肚子好痛啊\"与\"你的名字是什么\"的语义相似度为：0.3079039808785484\n",
      "\"我的肚子好痛啊\"与\"我的肚子好痛啊\"的语义相似度为：1.0\n",
      "\"我的肚子好痛啊\"与\"肠胃不舒服\"的语义相似度为：0.418081827009795\n",
      "\"我的肚子好痛啊\"与\"我在吃东西\"的语义相似度为：0.3523671162523911\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "def normalize(x):\n",
    "    x = np.asarray(x)\n",
    "    norms = np.sum(np.multiply(x, x))\n",
    "    norms = np.sqrt(norms)\n",
    "    return x / norms\n",
    "\n",
    "for i in range(5):\n",
    "    similarity = np.dot(normalize(embeddings[2]), normalize(embeddings[i]))\n",
    "    print(f'\"{texts[2]}\"与\"{texts[i]}\"的语义相似度为：{similarity}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# cache\n",
    "## LocalFileStore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.embeddings import CacheBackedEmbeddings\n",
    "from langchain.storage import LocalFileStore\n",
    "\n",
    "store = LocalFileStore(\"./cache/\")\n",
    "\n",
    "cached_embedder = CacheBackedEmbeddings.from_bytes_store(\n",
    "    embeddings_model, store, namespace=embeddings_model.model_name\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 75.1 ms, sys: 6.25 ms, total: 81.4 ms\n",
      "Wall time: 357 ms\n"
     ]
    },
    {
     "data": {
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       "  ...]]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "cached_embedder.embed_documents(texts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5.27 ms, sys: 2.35 ms, total: 7.62 ms\n",
      "Wall time: 6.12 ms\n"
     ]
    },
    {
     "data": {
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       "  -3.064648389816284,\n",
       "  0.7776041626930237,\n",
       "  1.297460913658142,\n",
       "  -0.827392578125,\n",
       "  -1.0642049312591553,\n",
       "  -0.26138508319854736,\n",
       "  -4.374479293823242,\n",
       "  0.24537760019302368,\n",
       "  1.012109398841858,\n",
       "  0.19007161259651184,\n",
       "  0.3000284731388092,\n",
       "  1.9489582777023315,\n",
       "  -5.096745014190674,\n",
       "  2.0503742694854736,\n",
       "  1.1049968004226685,\n",
       "  -0.8833333253860474,\n",
       "  -3.8958353996276855,\n",
       "  -5.300000190734863,\n",
       "  4.9505696296691895,\n",
       "  3.9369466304779053,\n",
       "  -1.4868489503860474,\n",
       "  -0.4463867247104645,\n",
       "  -0.8285156488418579,\n",
       "  -1.351293921470642,\n",
       "  0.5686197876930237,\n",
       "  1.6860026121139526,\n",
       "  3.613541603088379,\n",
       "  -6.0389323234558105,\n",
       "  0.5226145386695862,\n",
       "  0.6245442628860474,\n",
       "  -0.07224934548139572,\n",
       "  0.07669270783662796,\n",
       "  -2.2594075202941895,\n",
       "  0.013246663846075535,\n",
       "  4.4966959953308105,\n",
       "  0.20103353261947632,\n",
       "  -1.4306721687316895,\n",
       "  5.809895992279053,\n",
       "  0.8070312738418579,\n",
       "  0.49205729365348816,\n",
       "  -0.7813150882720947,\n",
       "  0.01256510429084301,\n",
       "  -0.7763671875,\n",
       "  -1.3672770261764526,\n",
       "  0.009212239645421505,\n",
       "  1.7382487058639526,\n",
       "  -2.5724267959594727,\n",
       "  -3.3223307132720947,\n",
       "  -2.854996681213379,\n",
       "  -0.6245035529136658,\n",
       "  1.3647135496139526,\n",
       "  -2.760025978088379,\n",
       "  1.1116211414337158,\n",
       "  -2.8540546894073486,\n",
       "  -2.43505859375,\n",
       "  -1.0998698472976685,\n",
       "  2.1842122077941895,\n",
       "  -2.2537922859191895,\n",
       "  -2.4138996601104736,\n",
       "  0.5443359613418579,\n",
       "  -2.7081217765808105,\n",
       "  -0.7852132320404053,\n",
       "  2.664290428161621,\n",
       "  -0.645703136920929,\n",
       "  ...]]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "cached_embedder.embed_documents(texts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['text_embedding_v1f33a10ff-859a-5463-b3ff-f49f9fa5f6fa',\n",
       " 'text_embedding_v1046ba0f1-f46d-50cb-a4a2-d42b4b0a372b',\n",
       " 'text_embedding_v1fdcb1804-6409-5e76-89ff-9684747fff9d',\n",
       " 'text_embedding_v17cd8ea1f-6312-57bd-b0c4-46b1e007af6a',\n",
       " 'text_embedding_v1c8a6a73e-11f0-59e7-84f4-cb126b59694f']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(store.yield_keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## InMemoryByteStore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.storage import InMemoryByteStore\n",
    "\n",
    "store = InMemoryByteStore()\n",
    "\n",
    "cached_embedder = CacheBackedEmbeddings.from_bytes_store(\n",
    "    embeddings_model, store, namespace=embeddings_model.model_name\n",
    ")\n",
    "\n",
    "cached_embedder.embed_documents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RedisStore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.storage import RedisStore\n",
    "\n",
    "store = RedisStore()\n",
    "\n",
    "cached_embedder = CacheBackedEmbeddings.from_bytes_store(\n",
    "    embeddings_model, store, namespace=embeddings_model.model_name\n",
    ")\n",
    "\n",
    "cached_embedder.embed_documents"
   ]
  }
 ],
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